Simple two‐stage weighting factor design for finite control set model predictive control of modular multilevel converters

Abstract

Due to discrete nature of power converters, finite control set model predictive control (FCS‐MPC) is considered as an attractive choice for these systems. Since this control technique provides advantages like improved dynamic performance and inclusion of several control objectives in a single cost function, it is especially appropriate for topologies like modular multilevel converters (MMCs), where multiple control goals, that is, output and circulating currents control, and cells' capacitor voltage balancing should be satisfied. Despite these advantages, huge computational burden and weighting factor selection are two serious obstacles. In this work, a simple two‐stage weighting factor selection scheme is suggested for FCS‐MPC applied to MMCs. At the first stage, some offline tests are conducted for different weighting factors; and a pre‐optimal value is selected, based on minimizing the total harmonic distortion (THD) of output current and root mean square (RMS) value of the circulating current. However, since optimal performance at different operating conditions is achieved by different weighting factors, the pre‐optimal value will be updated at the second stage by a simple online algorithm. Moreover, a technique is suggested for effectively decreasing the computational burden. Finally, by conducting several simulation and experimental tests, the satisfactory performance of proposed controller is validated, compared to fixed weighting factor FCS‐MPC.

Finite Control Set Model Predictive Control (FCS-MPC) for Enhancing the Performance of a Single-Phase Inverter in a Renewable Energy System (RES)

“…The mathematical and logical treatment of the latter can be accomplished within the framework of discrete time S. The model of the MLI is contingent upon the input DC-link voltage, capacitor voltages, and switching states. Once the prediction model is determined, it can be incorporated as the cost function [23]. The incorporation of weighting elements is crucial in the formulation of the cost function.…”

“…Among its variants, Finite Control Set Model Predictive Control (FCS-MPC) has garnered significant attention for controlling MLIs with superior performance, robustness, and accuracy. This approach enables the inverter to predict future behavior based on a discrete set of control signals and enables a precise modulation and high-speed response to system dynamics [18][19][20][21][22][23][24]. In the renewable energy sector, integrating FCS-MPC with MLIs holds significant potential to boost energy conversion efficiency, grid integration, and overall system reliability.…”

Generalized fuzzy cascade predictive functional control design for different types of challenging dynamics systems

“…Unstable systems are considered as another challenging dynamic system that is first stabilized via an inner loop of the proposed method, and afterward, the other control objectives, reference tracking, and disturbance rejection are obtained by the outer control loop. Another critical issue in all of model predictive methods is weighting factor adjustment due to their direct effect on results (Rossiter and Aftab, 2021; Dragievi and Novak 2019; Khosravi et al, 2021). In many studies, it is shown that the PFC can be combined well with fuzzy control techniques to deal with nonlinear uncertain systems (Chang and Yang, 2011).…”

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

“…However, they also suffer from higher computational burden and tend to increase the complexity of the controller. Miscellaneous methods such as twostage weighting factor tuning technique 67 and equal-weighted weighting factor selection technique 68 also tend to increase the computational cost of the control algorithms and are not feasible for practical implementation 47,[69][70][71][72][73] . In comparison the these methods, some of the MCDM methods do not pose computational challenges and are ideally aligned for implementation on the modern hardware 39,74,75 .…”